Prediction Residual Coding Parameters for Varying Autocorrelation
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Solution Overview
Problem
Conventional methods for variable length coding of prediction residuals in predictive coding schemes often result in parameters that deviate significantly from the optimum, especially in segments with varying autocorrelation of time-series signals, leading to inefficient coding and increased code length.
Innovation Solution
A technique where a positive second-segment parameter is calculated based on the average amplitude of prediction residuals in a subsequent time segment, and an additional value corresponding to the index representing prediction effectiveness is used as a parameter for variable length coding in the initial segment, ensuring more accurate parameter selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional fixed parameter methods are used for variable length coding of prediction residuals, then the coding process is simple, but the parameters deviate significantly from the optimum leading to increased code length and reduced coding efficiency
Solution Approach 1:
The time-series signal is divided into multiple time segments, and separate parameters are calculated for each segment based on local statistical characteristics. This segmentation allows the coding system to adapt to varying autocorrelation properties in different parts of the signal, resolving the contradiction between simple fixed-parameter coding and efficient adaptive coding.
Solution Approach 2:
The parameter for variable length coding is dynamically changed according to the statistical characteristics (average amplitude of prediction residuals) of each time segment. By calculating parameters locally rather than using a fixed global parameter, the system achieves optimal coding efficiency while adapting to signal variations, thereby reducing code length without sacrificing simplicity.
2Productivity
If adaptive parameter selection based on local signal characteristics is implemented, then coding efficiency improves and code length reduces, but the computational complexity and device complexity increase
Solution Approach 1:
Different parameters are assigned to different time segments based on local signal characteristics rather than using a uniform parameter throughout. The parameter for each segment is calculated from the local average amplitude of prediction residuals, making the complexity localized and manageable while achieving overall coding efficiency improvement.
Solution Approach 2:
The system uses the signal's own statistical characteristics (average amplitude of prediction residuals in each segment) to automatically determine the coding parameters for that segment. This self-adaptive approach eliminates the need for external complex optimization algorithms, reducing device complexity while maintaining high coding efficiency.
Data Source
AI summary
Provided that a first segment is an earliest time segment included in a discrete time segment, and a second segment is a time segment subsequent to the first segment, a positive second-segment parameter that corresponds to a weakly monotonically increasing function value of an average amplitude of prediction residuals in a time segment including the second segment is used as a parameter for variable length coding of prediction residuals in the second segment. In addition, a value that corresponds to a weakly monotonically increasing function value of the sum of the second-segment parameter and a positive additional value that corresponds to an index representing the prediction effectiveness of time-series signals in the time segment including the second segment is used as a parameter for variable length coding of the prediction residual at a certain discrete time in the first segment.